The invention pertains to digital data processing and, more particularly, to the visualization of image data. It has application, by way of non-limiting example, in medical imaging, microscopy, geophysics, non-destructive testing.
Data sets in diagnostic medical imaging and other disciplines such as microscopy, geo-physics, non destructive testing etc., are growing in size and number. Efficient visualization methods are therefore increasingly important to enable clinicians, researchers and others to analyze digital image data. Image segmentation and the extraction of structures from images for visualization and analysis can be helpful for this purpose.
Image segmentation is an automated technique that facilitates distinguishing objects and other features in digital images. The technique can be used, for example, to simplify digitized images so that they can be more readily interpreted by computers (e.g., image analysis software) and/or by their users. Thus, for example, image segmentation can be used to simplify a digitized x-ray image of a patient who has consumed a barium “milkshake.” In its original form, such an image is made up of pixels containing a wide range of undifferentiated intensity values that—although, possibly recognizable to the human eye as skeletal bones and digestive tract—are largely uninterpretable by a computer.
Image segmentation can remedy this by categorizing as being of potential interest (e.g., “not background”) all pixels of a selected intensity range—typically, all intensities above a threshold value. Alternatively, image segmentation can rely on finding all edges or borders in the image. A related, but still further alternative technique, is to identify all “connected components”—i.e., groupings of adjacent pixels in the image of the same, or similar, pixel intensity (or color). Yet another image segmentation technique involves “region growing,”in which connected components are grown around seed point pixels known to reside within structures of interest.
Continuing the example, threshold-based segmentation can be applied to an x-ray image such that pixels whose intensities are above, say, 200 (out of 255) are labelled as barium-containing digestive organs and all other pixels are labelled as background. If the pixel intensities of the former are uniformly adjusted to a common value of, say, 255, and the pixel intensities of the latter are uniformly adjusted to, say, 0, the resulting “segmented” image, with only two levels of intensity values (0 and 255) is often more readily interpreted by man and machine alike.
An object of the invention is to provide improved methods and apparatus for digital data processing.
A related object is to provide such improved methods and apparatus for the visualization of image data.
A still further related aspect of the invention is to provide such methods and apparatus as can be applied in medical imaging, microscopy, geophysics, non destructive testing, and other imaging applications.